Summary of Semantic Similarity Score For Measuring Visual Similarity at Semantic Level, by Senran Fan et al.
Semantic Similarity Score for Measuring Visual Similarity at Semantic Level
by Senran Fan, Zhicheng Bao, Chen Dong, Haotai Liang, Xiaodong Xu, Ping Zhang
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In a novel approach to image communication, researchers propose a semantic-based visual communication system that extracts, compresses, transmits, and reconstructs images at the semantic level. However, existing metrics struggle to accurately measure the loss of semantic-level information during transmission, making it challenging to evaluate performance. To address this, they introduce SeSS (Semantic Similarity Score), a graph-matching based metric that aligns with human perception. The proposed metric is tested on various datasets, including images transmitted at different compression rates and signal-to-noise ratios, as well as those generated by large-scale models with varying noise levels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine sending pictures to friends or family without worrying about the details of how they get there! A team of researchers has developed a new way to communicate images that focuses on what the picture means, rather than just its individual pixels. This approach is called “semantic communication.” The problem is that most ways we measure how well this works don’t take into account the important meaning behind the pictures. To solve this, they created a new metric called SeSS (Semantic Similarity Score) that measures how similar two images are in terms of their meaning. They tested this on lots of different types of images and showed that it’s a much better way to measure how well these kinds of communication systems work. |
